Investigating relationship between soil moisture, hydro-climatic parameters, vegetation, and climate change impacts in a semi-arid basin in Iran

Saadatabadi, Abbas Ranjbar; Izadi, Naser; Karakani, Elaheh Ghasemi; Fattahi, Ebrahim; Shamsipour, Aliakbar

doi:10.1007/s12517-021-07831-8

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Soil moisture (SM) is an essential parameter in agriculture [1], ecology [2], meteorology [3], and geology [4]. Accurately and timely monitoring SM changes is significant for crop growth assessment, water resource cycle problems, climate and weather forecasting, and geological hazard assessment.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Ding

Liang

et al. 2023

Remote Sensing

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Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When rm=0.600 and rn=0.700, the correlation coefficient (r) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology.

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Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Ding

Liang

et al. 2023

Remote Sensing

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“…Soil moisture is an essential component in the continental water-carbon cycle, and a key parameter for quantifying the energy and water exchange between land surface and atmosphere [1][2][3][4]. Precise soil moisture monitoring is an important foundation for achieving high yields of agriculture production.…”

Section: Introductionmentioning

confidence: 99%

Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach

Lili

Yang

et al. 2021

Remote Sensing

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This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasquère, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios.

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Evaluation of long-term changes in water balances in the Nepal Himalayas

Talchabhadel,

Chhetri

2023

Theor Appl Climatol

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Investigating relationship between soil moisture, hydro-climatic parameters, vegetation, and climate change impacts in a semi-arid basin in Iran

Cited by 5 publications

References 39 publications

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach

Evaluation of long-term changes in water balances in the Nepal Himalayas

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